Hospice Utilization Among Residents in Long-Term Care Facilities
Bibliographic record
Abstract
BACKGROUND: Hospice care can improve quality of life for persons nearing end of life, yet little is known about utilization of hospice care among persons residing in long-term care facilities (LTCFs). Given the increasing number of deaths that occur in LTCFs, it is important to examine hospice care practices in LTCFs. AIM: The aim of the cross-sectional study was to describe residents who received hospice care in LTCFs and explore factors that can predict hospice use in LTCFs across Canada. This study included 185 715 residents aged 19 years or older in LTCFs in Canada in 2015. RESULTS: Of all residents, 2.7% (n = 4973) received hospice care and 6.8% (n = 12 684) were profiled as having an end-stage disease. Among those who received hospice care, most were noted as end stage (89.5%) and had severe physical impairment (Activities of Daily Living Hierarchy Scale ≥ 5, 74.3%), mild-to-severe pain (Pain Scale ≥ 1, 76.0%), and moderate-to-severe health instability (Changes in Health, End-Stage Disease, Signs, and Symptoms Scale ≥3, 82.9%). Residents who received hospice care were in more severe and complex clinical conditions than those who did not receive hospice care. CONCLUSION: Only a small proportion of residents in LTCFs received hospice care. Further investigation of standardized assessment of terminal status is needed as accuracy of end-stage diagnosis continues to be challenging and criteria for hospice eligibility are narrow. Special attention should be paid to improve access to hospice care among residents with dementia or other progressive chronic diseases with severe and complex clinical needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".